Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

Lee, Jay; Alnasrallah, Mohammad; Wong, David W. S.; Beaird, Heather; Logue, Everett

doi:10.3390/ijgi3041198

Cited by 12 publications

(8 citation statements)

References 26 publications

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“…On the other hand, the content of heavy metals in soil are often spatially clustered and show spatial autocorrelation in addition to global trends. Thus, local variability-based modeling methods, such as geographically weighted regression and cokriging interpolation in geostatistics, would provide the potential to improve the prediction accuracy of Cd, Hg, and As contents [12,52,53].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and of 26 croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

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Section: Discussionmentioning

confidence: 99%

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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“…Any geographical analysis might thereby be influenced in terms of scale, i.e., the chosen size of the spatial entities, and/or zoning, i.e., the actual spatial arrangement of the respective entities [192]. In the case of environmental studies, the effects of MAUP can change the magnitude and distribution of environmental inequality in urban areas drastically [193,194]. To avoid the problem of aggregation bias, Fotheringham & Wong ([191], p. 1042) suggest "to avoid the use of aggregated data where possible".…”

Section: Ecological Fallacy and The Modifiable Areal Unit Problemmentioning

confidence: 99%

Remote Sensing in Environmental Justice Research—A Review

Weigand

Wurm

Dech

et al. 2019

IJGI

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Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.

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“…It represents a mid‐sized American city that lost much of its former industrial base as de‐industrialization occurred and today has many of the same problems as other cities in the North American manufacturing belt. Summit County, where Akron is located, has been the study setting for research on obesity patterns as its demographic and socio‐economic profiles are very close to those of the nation (Lee, Alnasrallah, Wong, Beaird, & Logue, ), and access to healthy food is considered a factor in obesity rates (Ghosh‐Dastidar et al, ).…”

Section: Case Study Setting Data Methods and Resultsmentioning

confidence: 99%

Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices

Cromley

2019

Transactions in GIS

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Access to certain types of facilities can promote health and well-being. When population and facilities are not uniformly distributed across the landscape, inequities in accessibility may occur. Current research into GIS-based accessibility measures has focused primarily on spatial inequities between different geographic locations but not directly on differences in accessibility between subgroups of the population. The research presented here develops a new method for measuring differential accessibility to facilities between various segments of the population. The method extends concepts and techniques in spatial point pattern analysis that account for the spatial structure of demand and its relationship to supply. In this approach, the traditional Lorenz curve and its associated indices, the Gini coefficient and the dissimilarity index, which are used to measure inequality, are recast in spatial terms for measuring differences in accessibility between population subgroups.An analysis of spatial accessibility to grocery stores in Akron, OH illustrates the value of the spatial Lorenz curve and its associated indices compared to other methods.

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Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

Cited by 12 publications

References 26 publications

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Remote Sensing in Environmental Justice Research—A Review

Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices

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